| Summary: | This study investigates the mechanism by which individuals learn to associate signals with meanings in a way that is agreeable to everyone, and thereby, to collectively produce common and stable signaling systems. Previous studies suggest that simple learning algorithms based on local interactions, such as reinforcement learning, sufficiently give rise to signaling systems in decentralized populations. However, those algorithms often fail to achieve optimal signaling systems. Under what condition do suboptimal signaling systems emerge? To address this question, we propose a multi-agent model of signaling games with three parameters–memory length, the complexity of communication problems, and population size–as potential constraints imposed on the collective learning process. The results from numerical experiments suggest that finite memory leads to suboptimal signaling systems, characterized by redundant signal-meaning associations. This paper concludes with discussions on the theoretical implications of the findings and the directions of future research.
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